What Is ETL? (Definition, Importance And Prominent Uses)

By Indeed Editorial Team

Updated 7 September 2022 | Published 14 May 2022

Updated 7 September 2022

Published 14 May 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

In computing, the general procedure is to extract, transform and load when copying data from one or more sources into a destination system. This represents the data differently from the source or in a different context than the source. The ETL process became a popular concept as companies and independent users started using it for data warehousing. In this article, we examine what ETL is, how it works and what its prominent uses are.

What Is ETL?

The answer to the question, ‘What is ETL?' is that it stands for extract, transform and load. It is a data integration process that combines data from numerous sources into a single, consistent data stored in a data warehouse or destination system. ETL helps you build work streams for data analytics and machine learning. ETL cleanses and organises data using a set of business rules to meet specific business intelligence objectives, such as monthly reporting. It can also handle more advanced analytics to improve back-end processes or end-user experiences.

Related: Learn About Data Science Careers (With Skills And Duties)

How Does ETL Work?

ETL systems can perform three difficult database processes. They are the following:

Extraction

A module extracts data from a variety of data sources, regardless of file type. For example, banking and insurance technology platforms use distinct databases, hardware, operating systems and communication protocols. Their system gathers information from a variety of sources, including ATMs, text files, PDFs, spreadsheets and scanned forms. Before processing, the extraction step translates data from several sources into a consistent format.

While extracting data, ETL systems assure the following:

  • Removing data that is redundant or fragmented

  • Getting rid of spam and unnecessary data

  • Reconciling documents with their original sources

  • Examining data types and important attributes

Transformation

This stage includes the application of algorithms and the modification of data in accordance with business-specific regulations. Computation, concatenation, filters and string operations such as currency, time and data type are all frequent operations in the ETL transformation step. It also supports the following:

  • Data cleansing, like adding a '0' to null values

  • Threshold validation, like verifying that age does not exceed two digits

  • Standardisation of data using rules and a lookup table

  • Data conversion in ETL

Loading

Loading is the process of moving data sets into a data warehouse. This process can help move large amounts of data in a very short period of time. ETL solutions are essential for speeding up the loading process and offering appropriate recovery mechanisms in the event of loading errors.

There are three types of loading functions in a typical ETL process:

  • Initial load: It fills the data warehouse with records.

  • Incremental load: It implements changes regularly under specific requirements.

  • Full refresh: It reloads the warehouse with new records by wiping the previous contents.

Importance Of ETL In Business Intelligence

Businesses use the ETL process to get comprehensive data volumes that can help them make better decisions. The following ETL features support this assertion:

High-level data mapping

Leveraging data and converting them into usable insights is a challenge with dispersed and extensive data. Integration, migration, warehousing and transformation are all easier with data mapping. ETL helps map data for specialised applications that are compatible with it. Data mapping aids in establishing a link between several data models.

Data quality and big data analytics

Large volumes of data may not be very useful in an unprocessed state. When you apply algorithms to raw data, the results may be unclear. To acquire useful insights, it is necessary to properly structure, analyse and interpret the data volume. ETL also optimises the warehouse's data quality by standardising and deleting duplicates.

Related: 18 Big Data Examples (Common Uses In Different Industries)

Automation and faster batch data processing

Scripts, rather than traditional programming, power current ETL solutions. Scripts are a small set of instructions that run in the background and perform certain repetitive or predetermined tasks. ETL also conducts 'batch' data processing, such as transferring large amounts of data between two systems routinely. The amount of data coming into a system can reach millions of occurrences per second on rare occasions. Data monitoring and batch processing can assist in making prompt decisions in such circumstances. Banks, for example, batch process data to resolve the whole day's transactions, usually late at night.

Uses Of ETL

These are some prominent uses of ETL:

ETL and traditional uses

Businesses can use ETL technologies to mix structured and unstructured data from many sources and store it in a data warehouse. ETL transforms raw data into table formats that are optimal for reporting, allowing previously hidden insights to surface, using analytics or visualisation software. ETL can, for example, merge name, location and pricing data from business operations with transactional data. ETL also aids data migration from legacy warehouses to the cloud. It can also combine data from two independent sources. For example, as part of a business merger, ETL can help merge data from two independent business entities.

ETL with big data

Operational and transactional data is only a small part of the information that most businesses gather. The quantity of big data streaming into businesses via the Internet, social media, video, log mining and other such sources is a lot larger in comparison. Businesses‌ use this range of data to gain a competitive advantage, comprehend context and make informed decisions. As data types and data sources change and increase in volume, ETL suppliers may continually introduce new transformation capabilities and interfaces to keep up with larger conversion or transformation workloads.

ETL for Hadoop

Traditional data warehouses, structured master data and traditional ETL methods may slowly phase out in the future. Many people, for example, use Hadoop to load and convert their structured and unstructured data, and they are increasingly doing it on the cloud. Data engineers can improve the speed and scalability of their ETL processes by using Hadoop. Traditional data warehousing scales significantly slower than a centralised Hadoop repository. Hadoop is also open-source and is a low-cost alternative to traditional data technology for many businesses. For faster data processing in Hadoop, certain Hadoop-friendly ETL systems contain pre-built libraries of ETL operations.

Related: What Is Big Data Hadoop? (Definition And Career Opportunities)

Common ETL Challenges

These are some common challenges that you may encounter while working with ETL:

Scaling

One of the most significant qualities of a modern ETL tool is scalability. The amount of data that businesses collect may only increase in the future. Moving data into batches now might not be the best decision for a company at a later point in time. If at all practical, users may be able to grow ETL procedures and storage capacity indefinitely. However it is beneficial to use the cloud to your advantage and think big and fast with ETL and data.

Transforming data accurately

Another significant ETL challenge is guaranteeing the accuracy and completeness of the data you transform. Manual coding and revisions, and a failure to design and test an ETL workflow before running it, can result in problems such as loading duplicates, missing data and other execution issues. An ETL tool can assist in eliminating the need for manual coding and subsequently reducing errors. Data accuracy testing and monitoring features can assist in uncovering inconsistencies and duplicates, instances of incompatible data types and other common data management issues.

Handling diverse data sources

Both volume and complexity of data are increasing. A single multinational company typically manages thousands of data sources. Structured and semi-structured sources, real-time sources, flat files, CSVs, S3 buckets and streaming sources all present possibilities for data extraction. It is best to process some data volumes in batches, while others benefit from continuous, streaming data transformation. It can be a daunting task to handle each type of data in the most efficient and practical way possible.

Internet of things data integration

Devices and systems that are part of the Internet of Things are currently one of the fastest increasing sources of data for businesses. Whether it is wearable gadgets or embedded sensors in places, vehicles or equipment, the Internet of Things generates massive amounts of data. Integration and transformation of data from IoT sources may require ELT technology, particularly cloud-native ETL.

Database replication

ETL is also essential for database replication, whether it is a one-time operation or a continuous procedure. Moving data from an on-premises data warehouse to a cloud data warehouse is common, but as more businesses go to the cloud, it may also require switching cloud infrastructure or cloud service providers. This is why having an ETL or ELT solution that not only works in the cloud but also across many cloud platforms is critical.

Please note that none of the companies, institutions or organisations mentioned in this article are associated with Indeed.

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